- A
The network latency between the client and the endpoint has increased due to regional issues.
Why wrong: While network latency could affect overall latency, the high CPU utilization points to a compute bottleneck inside the endpoint.
- B
The model is deployed with GPU acceleration, but the instances are using incorrect CUDA drivers.
Why wrong: There is no mention of GPU usage; the symptom is high CPU, not driver-related errors.
- C
The model is too large for the instance memory, causing disk swapping.
Why wrong: Disk swapping would cause high disk I/O, not just high CPU; also memory issues would likely cause errors.
- D
The model is CPU-bound, and the current replicas are saturated, causing queuing.
High CPU utilization indicates the replicas are at capacity, leading to request queuing and higher latency.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team notices that the latency for online predictions from a Vertex AI endpoint has increased significantly over the past hour. The model is a large TensorFlow model deployed with automatic scaling (minReplicaCount=2, maxReplicaCount=10). The CPU utilization of the deployed instances is consistently above 85%. What is the most likely cause of the increased latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The model is CPU-bound, and the current replicas are saturated, causing queuing.
The correct answer is D because the consistently high CPU utilization (above 85%) indicates that the existing replicas are saturated, unable to process incoming requests quickly enough. When all replicas are busy, new requests are queued, which directly increases latency. Automatic scaling can add more replicas up to maxReplicaCount=10, but if the scaling is slow or the traffic spike is sudden, queuing occurs first, causing the observed latency increase.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The network latency between the client and the endpoint has increased due to regional issues.
Why it's wrong here
While network latency could affect overall latency, the high CPU utilization points to a compute bottleneck inside the endpoint.
- ✗
The model is deployed with GPU acceleration, but the instances are using incorrect CUDA drivers.
Why it's wrong here
There is no mention of GPU usage; the symptom is high CPU, not driver-related errors.
- ✗
The model is too large for the instance memory, causing disk swapping.
Why it's wrong here
Disk swapping would cause high disk I/O, not just high CPU; also memory issues would likely cause errors.
- ✓
The model is CPU-bound, and the current replicas are saturated, causing queuing.
Why this is correct
High CPU utilization indicates the replicas are at capacity, leading to request queuing and higher latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between symptoms of CPU saturation (queuing/latency) versus memory or GPU issues; the trap here is that candidates may incorrectly attribute latency to network or hardware driver problems when the clear indicator is sustained high CPU utilization on existing instances.
Detailed technical explanation
How to think about this question
Vertex AI automatic scaling uses a target CPU utilization metric (default 60%) to decide when to add or remove replicas. When CPU utilization exceeds this threshold, the autoscaler adds instances, but there is a cooldown period (typically 60 seconds) before new replicas become ready. During a sudden traffic surge, requests queue up in the load balancer or at the model server (e.g., TensorFlow Serving's gRPC thread pool), causing latency to spike before new replicas are online. This is a classic 'thundering herd' scenario where scaling lags behind demand.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model is CPU-bound, and the current replicas are saturated, causing queuing. — The correct answer is D because the consistently high CPU utilization (above 85%) indicates that the existing replicas are saturated, unable to process incoming requests quickly enough. When all replicas are busy, new requests are queued, which directly increases latency. Automatic scaling can add more replicas up to maxReplicaCount=10, but if the scaling is slow or the traffic spike is sudden, queuing occurs first, causing the observed latency increase.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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